Variable selection for the determination of total polar materials in fried oils by near infrared spectroscopy

2018 ◽  
Vol 27 (2) ◽  
pp. 107-114 ◽  
Author(s):  
Mari M Cascant ◽  
Salvador Garrigues ◽  
Miguel de la Guardia
Keyword(s):  
Variable Selection ◽  
Near Infrared ◽  
Low Cost ◽  
Oil Quality ◽  
Analytical Techniques ◽  
Coefficient Of Determination ◽  
Prediction Errors ◽  
Polar Materials ◽  
Selection Of

Total polar materials (TPM) content is considered as the best indicator and the most common parameter to check the quality of deep-frying oils. The development of simpler and quicker analytical techniques than the available methods to monitor oil quality in restaurants and fried food outlets is an important topic related to the human health. This paper reports a comparison of the variable selection of near infrared (NIR) spectra by multiple linear regression (MLR-NIR) with partial least squares (PLS-NIR) models for the quantification of TPM in fried vegetable oils. The use of PLS-NIR offers an alternative in laboratory bench equipment for the determination of TPM in oils employed for frying different kinds of foods with relative prediction errors of 6.5%, a coefficient of determination for prediction of 0.99 and a residual predictive deviation (RPD) of 9.2 when selected wavenumber intervals were employed. MLR-NIR allows the selection of a reduced number of wavenumber in order to develop low cost instruments to evaluate the frying oil quality. Based on the NIR signals at four wavenumbers, the relative prediction error was 12.1%, the coefficient of determination for prediction was 0.96 and the RPD was 5.0.

Nondestructive NIR and NIT Determination of Protein, Fat, and Water in Plastic-Wrapped, Homogenized Meat

1992 ◽  
Vol 46 (11) ◽  
pp. 1685-1694 ◽  
Author(s):  
Tomas Isaksson ◽  
Charles E. Miller ◽  
Tormod Næs
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Errors ◽  
Optimal Test ◽  
Meat Samples ◽  
Water Contents

In this work, the abilities of near-infrared diffuse reflectance (NIR) and transmittance (NIT) spectroscopy to noninvasively determine the protein, fat, and water contents of plastic-wrapped homogenized meat are evaluated. One hundred homogenized beef samples, ranging from 1 to 23% fat, wrapped in polyamide/polyethylene laminates, were used. Results of multivariate calibration and prediction for protein, fat, and water contents are presented. The optimal test set prediction errors (root mean square error of prediction, RMSEP), obtained with the use of the principal component regression method with NIR data, were 0.45, 0.29 and 0.50 weight % for protein, fat, and water, respectively, for plastic-wrapped meat (compared to 0.40, 0.28 and 0.45 wt % for unwrapped meat). The optimal prediction errors for the NIT method were 0.31, 0.52 and 0.42 wt % for protein, fat, and water, respectively, for plastic-wrapped meat samples (compared to 0.27, 0.38, and 0.37 wt % for unwrapped meat). We can conclude that the addition of the laminate only slightly reduced the abilities of the NIR and NIT method to predict protein, fat, and water contents in homogenized meat.

scholarly journals Fast Determination of the Rubber Content in Taraxacum Kok-Saghyz Fresh Biomass Using Portable Near Infrared Spectroscopy and Pyrolysis-Gas Chromatography

2021 ◽  
Author(s):  
Ying Chen ◽  
Dong Yiyang ◽  
Xiang Ma ◽  
Jiaru Li ◽  
Minmin Guo ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Infrared Spectroscopy ◽  
Natural Rubber ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Low Cost ◽  
Rubber Content ◽  
Pyrolysis Gas ◽  
Pyrolysis Gas Chromatography

Abstract Background: Taraxacum kok-saghyz (TKS), a plant native to the Tianshan valley on the border between China and Kazakhstan and inherently rich in natural rubber, inulin and other bioactive ingredients, is an important industrial crop. TKS rubber is a good substitute for natural rubber. TKS's breeding work necessitates the need to screen high-yielding varieties, hence rapid determination of rubber content is essential for the screening. Conventional analytical methods cannot meet actual needs in terms of real-time testing and economic cost. Near-infrared spectroscopy analysis technology, which has developed rapidly in the field of industrial process analysis in recent years, is a green detection technology with obvious merits of fast measurement speed, low cost and no sample loss. This research aims to develop a portable non-destructive near-infrared spectroscopic detection scheme to evaluate the content of natural rubber in TKS fresh roots. Pyrolysis gas chromatography (PyGC), was chosen as the reference method for the development of NIR prediction model. Results: 208 TKS fresh root samples were collected from the Inner Mongolia Autonomous Region of China. Near-infrared spectra were acquired for all samples. Randomly two-thirds of them were selected as the calibration set, the remaining one-third as the verification set, and the partial least squares method was successfully used to establish a good NIR prediction model at 1080-1800nm with a performance to deviation ratio (RPD) of 5.54 and coefficient of determination (R2) of 0.95. Conclusions: This study showed that portable near-infrared spectroscopy could be used with ease for large-scale screening of TKS plants in farmland, and could greatly facilitate TKS germplasm preservation, high-yield cultivation, environment-friendly, high-efficiency and low-cost rubber extraction, and comprehensive advancement of the dandelion rubber industry thereof.

Determination of API Gravity and Total and Basic Nitrogen Content by Mid- and Near-Infrared Spectroscopy in Crude Oil with Multivariate Regression and Variable Selection Tools

2019 ◽  
Vol 52 (18) ◽  
pp. 2914-2930 ◽  
Author(s):  
Karla Pereira Rainha ◽  
Júlia Tristão do Carmo Rocha ◽  
Rayza Rosa Tavares Rodrigues ◽  
Betina Pires de Oliveira Lovatti ◽  
Eustáquio Vinicius Ribeiro de Castro ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Crude Oil ◽  
Nitrogen Content ◽  
Near Infrared Spectroscopy ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Basic Nitrogen ◽  
Selection Tools

scholarly journals Measurement System for Lossy Capacitive Sensors: Application to Edible Oils Quality Assessment

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4299 ◽  
Author(s):  
Ahmed Fendri ◽  
Ahmed Yahia Kallel ◽  
Hanen Nouri ◽  
Hamadi Ghariani ◽  
Olfa Kanoun
Keyword(s):  
Measurement System ◽  
Relative Permittivity ◽  
Edible Oils ◽  
Low Cost ◽  
Oil Quality ◽  
Capacitive Sensor ◽  
Polar Compounds ◽  
Coefficient Of Determination ◽  
Chemical Parameters ◽  
Dielectric Parameters

This paper aimed to develop a portable, low-cost, and easy-to-use measurement system for oil quality degradation assessment. The main two chemical parameters affected by frying are the total polar compounds (TPC) and free fatty acids. The system should characterize the change of chemical parameters by measuring the changes in its dielectric parameters. The dielectric parameters, relative permittivity, and conductivity are measured by measuring the capacitance and resistance of a capacitive sensor dipped in oil. The main challenges are that the corresponding changes of the capacitance and resistance are very small and the presence of stray effects. For this reason, the measurement system should be able to detect changes in capacitance and resistance with high resolution and with good immunity to stray effects. The proposed measurement system is based on the conversion of impedance to voltage and time and combining, therefore, having two measurement methods in one circuit. In this way, it is possible to measure the dielectric and resistive parameters and not only the relative permittivity as was done in previous works. The results showed a strong correlation between the chemical and electrical parameters with a coefficient of determination in the range of 0.9.

scholarly journals Design and Field Campaign Validation of a Multirotor UAV and Optical Particle Counter

2020 ◽  
Author(s):  
Joseph Girdwood ◽  
Helen Smith ◽  
Warren Stanley ◽  
Zbigniew Ulanowski ◽  
Chris Stopford ◽  
...  
Keyword(s):  
Low Cost ◽  
Unmanned Aircraft ◽  
Coefficient Of Determination ◽  
Sampling System ◽  
Particle Counter ◽  
Representative Sampling ◽  
Atmospheric Particulates ◽  
Optical Particle Counter ◽  
Good Agreement

Abstract. Small unmanned aircraft (SUA) have the potential to be used as platforms for the measurement of atmospheric particulates. The use of an SUA platform for these measurements provides benefits such as high manoeuvrability, re-usability, and low-cost when compared with traditional techniques. However, the complex aerodynamics of an SUA (particularly for multirotor airframes), combined with the miniaturisation of particle instruments poses difficulties for accurate and representative sampling of particulates. The work presented here relies on computational fluid dynamics with Lagrangian particle tracking (CFD-LPT) simulations to influence the design of a bespoke meteorological sampling system: the UH-AeroSAM. This consists of a custom built airframe, designed to reduce sampling artefacts due to the propellers, and a purpose built open-path optical particle counter–the Ruggedised Cloud and Aerosol Sounding System (RCASS). OPC size distribution measurements from the UH-AeroSAM are compared with the Cloud and Aerosol Precipitation Spectrometer (CAPS) for measurements of Stratus cloud during the Pallas Cloud Experiment (PaCE) in 2019. Good agreement is demonstrated between the two instruments. The integrated dN/dlog(Dp) is shown to have a coefficient of determination of 0.8, and a regression slope of 0.9 when plotted 1:1.

Near infrared spectroscopy for rapid determination of solids content of amino resins

2020 ◽  
Vol 28 (5-6) ◽  
pp. 344-350
Author(s):  
M Gonçalves ◽  
NT Paiva ◽  
JM Ferra ◽  
J Martins ◽  
F Magalhães ◽  
...  
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Water Addition ◽  
Reliable Technique ◽  
Standard Normal Variate ◽  
And Performance ◽  
Solids Content

Near infrared (NIR) spectroscopy is a fast and reliable technique for assessing properties of amino resins. One important property that defines the cost and performance of these resins is the solids content (SC). This work studied the prediction of SC of amino resins by combining NIR spectroscopy with partial least squares (PLS) regression. A total of 990 industrial NIR spectra of amino resins were obtained and split randomly by a ratio of 2/3 for calibration and 1/3 for validation. The best model achieved a root mean-square error of prediction (RMSEP) of 0.32% (m/m) and a coefficient of determination of prediction ([Formula: see text]) of 81%. standard normal variate (SNV) was found to be the NIR pre-processing that provided the best results for model construction. Addition of water to two amino resins showed that the NIR model does not respond to the water addition, despite water making great contribution to the SC value. An inference that can be obtained from this is that the NIR model of amino resins uses NIR properties of amino resins that relate to the SC and from there predict the most probable SC, instead of looking at all the components that affect the SC of amino resins.

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